Predicting Patients at Risk of Being Hospitalized

Fantastic initiative launched by Heritage Provider Network, a US managed care organization. 

They believe very strongly that predictive analytics can help them significantly reduce the number of unnecessary (or preventable) hospitalizations.  They are backing this belief to the tune of $3 million in prize money, offering it to anyone in the world who can find and exploit the most useful patterns in health records and claims data to help predict who is most at risk of early hospitalization. They believe there is $30 Billion per year being spent in the US alone on unnecessary health admissions - so in many ways, the more obvious question becomes why hasn't anyone done this before? I imagine that we will begin to see a lot more of this sort of thing, following in the footsteps of last years NetFlix Challenge where NetFlix offered a prize of $1 million to the team/individual who was able to improve NetFlix's ability to predict which movie someone was likely to want to like (again based upon patterns in their data).

 

To quote from www.theheritagehealthprize.com :

More than 71 Million individuals in the United States are admitted to hospitals each year, according to the latest survey from the American Hospital Association.  Studies have concluded that in 2006 well over $30 billion was spent on unnecessary hospital admissions.  How many of those hospital admissions could have been avoided if only we had real-time information as to which patients were at risk for future hospitalization?  This is more than just an academic question:  every unnecessary admission to the hospital places the patient at risk and uses scarce medical resources unwisely.


The Heritage Provider Network (HPN) launched the $3 million Heritage Health Prize with one goal in mind: to develop a breakthrough algorithm that uses available patient data, including health records and claims data, to predict and prevent unnecessary hospitalizations.  Heritage believes that incentivized competition – one that includes the involvement of those with passionate minds that don’t know what can’t be done – is the best way to achieve the radical breakthroughs and innovations necessary to reform our health care system.  Sponsoring this prize is simply one way that Heritage believes it can help solve a societal problem.


The winning Team will create a predictive algorithm that can identify patients who are at risk for hospital admissions.  Once known, health care providers can develop new care plans and strategies to reach patients before emergencies occur, thereby reducing the number of unnecessary hospitalizations.  This will result in increasing the health of patients while decreasing the cost of care.  In short, a winning solution will change health care delivery as we know it – from an emphasis on caring for the individual after they get sick to a true HEALTH care system.

Predictive Analytics to Detect Missed Charges in a Hospital System

Hospitals are complex environments with numerous charges for a multitude of small items, all administered by humans, so in the industry it is just about considered a given that some charges will slip through the cracks.  Karen Minich-Pourshadi recently wrote a very interesting article about a hospital in Washington which was able to pick up one million dollars of revenue just in the first 90 days of implementing a predictive analytics system to detect which hospital bills were most likely to have been undercharged – charge recovery. There were two benefits to it, the transactions were detected and corrected prior to billing, and it highlighted the areas and doctors most at risk of under-charging so that they could focus on improving in the future.

“For instance, the system flagged specific diagnosis which usually have lab tests associated with them if the lab test codes were missing. In doing so, they were able to capture all the charges associated with a diagnosis and then alert clinicians to be aware of their mistakes.”

This is a great example of searching for patterns in the screeds of data residing in an organization, to deliver massive value. This example of course doesn’t only apply to healthcare, but to any complex billing system that is handled by humans. Conversely you  can imagine that this same approach is very useful for those who are paying the hospital bills (i.e. insurance companies) – those parties of course are more interested in identifying over-billing rather than under-billing.

 At 11Ants Analytics we have recently done some interesting work, the specific application is unfortunately confidential by the customers request, but it is in the space of combing millions of transactions, looking for anomalies which yield up opportunities for major savings.

The advantages of taking a predictive analytics approach of learning from the patterns in the data, as opposed to a rules-based approach, is that a rules based approach requires knowing every potential problem area before starting, while with a predictive analytics approach, there are no assumptions going into it, and the patterns are ‘learned’ whatever they may be. The other benefit is that deployment of the solution is usually much faster, both on the development side and the implementation side as it is used every month.